Oscillatory theta activity in a fronto‐parietal network has been associated with working memory (WM) processes and may be directly related to WM performance. In their seminal study, Polanía et al. (2012) (de‐)coupled a fronto‐parietal theta‐network by applying transcranial alternating current stimulation (tACS), and showed that anti‐phase tACS led to slower and in‐phase tACS to faster response times in a verbal WM task compared to placebo stimulation. In the literature, this ‘synchronization‐desynchronization’ effect has only been partly replicated, and electric field modelling suggests that it might not be the fronto‐parietal network that is primarily stimulated during in‐phase tACS with a shared return electrode. This provides one possible reason for inconsistency in the literature. In this study, we aimed to reproduce the findings reported by Polanía et al. (2012). We also aimed to investigate whether in‐phase theta tACS with multiple close‐by return electrodes for focal stimulation of the frontal and the parietal cortex will have at least as much of a facilitatory effect as the in‐phase stimulation as indicated by Polania et al. (2012). In a single‐trial distributional analysis, we explored whether mean, variation and right‐skewness of the response time distribution are affected. Against our hypothesis, we found no ‘synchronization‐desynchronization’ effect by fronto‐parietal theta tACS on response times using the same delayed letter discrimination task and stimulation parameters in two experiments, both between‐subjects and within‐subjects. However, we could show that in a more demanding 3‐back task, fronto‐parietal in‐phase and in‐phase focal theta tACS substantially improved task performance compared to placebo stimulation.

Background: To interact successfully with their environment, humans need to build a model to make sense of noisy and ambiguous inputs. An inaccurate model, as suggested to be the case for people with psychosis, disturbs optimal action selection. Recent computational models, such as active inference, have emphasized the importance of action selection, treating it as a key part of the inferential process. Based on an active inference framework, we sought to evaluate previous knowledge and belief precision in an action-based task, given that alterations in these parameters have been linked to the development of psychotic symptoms. We further sought to determine whether task performance and modelling parameters would be suitable for classification of patients and controls. Methods: Twenty-three individuals with an at-risk mental state, 26 patients with first-episode psychosis and 31 controls completed a probabilistic task in which action choice (go/no-go) was dissociated from outcome valence (gain or loss). We evaluated group differences in performance and active inference model parameters and performed receiver operating characteristic (ROC) analyses to assess group classification. Results: We found reduced overall performance in patients with psychosis. Active inference modelling revealed that patients showed increased forgetting, reduced confidence in policy selection and less optimal general choice behaviour, with poorer action–state associations. Importantly, ROC analysis showed fair-to-good classification performance for all groups, when combining modelling parameters and performance measures. Limitations: The sample size is moderate. Conclusion: Active inference modelling of this task provides further explanation for dysfunctional mechanisms underlying decision-making in psychosis and may be relevant for future research on the development of biomarkers for early identification of psychosis.